Analyzing the Impact of Affirmative Action on Stanford Demographics: A Study of Gender, Race, and Ethnicity from 1980-2021¶

Introduction¶

Affirmitive Action, passed on September 24,1965, refers to policies and measures implemented by governments, organizations, or institutions to address historical or ongoing discrimination and promote equal opportunities for underrepresented groups, typically in the areas of employment, education, and public contracting. On June 29, 2023 the Supreme Court ruled on affirmitive action and called for the end of race-conscios admission practices in higher education.

This report examines the effects of affirmative action on the racial and ethnic demographics of Stanford University from 1980 to 2021. California abolished affirmative action for higher education in 1996 with the passage of Proposition 209. This report focuses on analyzing the effects of the absence of affirmative action on Stanford University, delving into the changes in racial and ethnic composition over time. Through exploratory data analysis the following questions will be addressed:

  1. How has the racial and ethnic composition of Stanford University changed over time, particularly after the abolishment of affirmative action in California?
  2. What are the enrollment trends and representation of different racial and ethnic groups in Stanford's student body?
  3. Are there any discernible disparities or under-representation of certain minority groups in the absence of affirmative action?
  4. What are the potential implications of these findings for Stanford University and the broader educational landscape?

By analyzing historical data and employing statistical techniques, this report aims to provide insights into the impact of affirmative action on Stanford's demographics, contributing to a broader understanding of the educational system's role in promoting diversity and equal opportunities for historically marginalized groups.

Preparing the Data¶

In [1]:
# Import the Necessary Libraries 
import pandas as pd 
import plotly.express as px
from plotly.subplots import make_subplots
import plotly.graph_objects as go

The concept of race and ethinicity classification is ever changing. As our society and population has changed so has the methods and language used to classify race and ethinicity in the United States. For accuracy and consistency throughout this report the data pulled from the Nationcal Center of Eduation Statistics was divided into two timeframes, 1980-2007 and 2010-2021.

Before 2010, racial groups such as Asian and Pascific Islanders were lumped together into one category where as in recent studies Asian classifies as one group and Native Hawaiian or Other Pacific Islander is classified as a completely different group. Other changes such as the addition of more categories such as a category for people who identify with two or more races was also added in more recent studies following 2010.

In [2]:
# Import the .csv File to Be Read 
df_demographics = pd.read_csv('Stanford Demographics 1980-2007.csv')
pd.set_option('display.max_columns',None)


# Rename Column Headers for Clarity 
df_demographics = df_demographics.rename(columns={'year':'Year',
                                         'EF1980_A.Grand total': 'Grand Total',
                                         'EF1980_A.Total men, reported':'Grand Total Men',
                                          'EF1980_A.Total women, reported':'Grand Total Women',
                                         'EF1980_A.White non-Hispanic total':'White Non-Hispanic Total',
                                         'EF1980_A.White non-Hispanic men':'White Non-Hispanic Men',
                                         'EF1980_A.White non-Hispanic women':'White Non-Hispanic Women',
                                         'EF1980_A.Black non-Hispanic  total':'Black Non-Hispanic Total',
                                         'EF1980_A.Black non-Hispanic men':'Black Non-Hispanic Men',
                                         'EF1980_A.Black non-Hispanic women':'Black Non-Hispanic Women',
                                         'EF1980_A.Hispanic total':'Hispanic Total',
                                         'EF1980_A.Hispanic men':'Hispanic Men',
                                         'EF1980_A.Hispanic women':'Hispanic Women',
                                         'EF1980_A.Asian or Pacific Islander total':'Asian or Pacific Islander Total',
                                         'EF1980_A.Asian or Pacific Islander men':'Asian or Pacific Islander Men',
                                         'EF1980_A.Asian or Paciific Islander women':'Asian or Pacific Islander Women',
                                         'EF1980_A.American Indian or Alaska Native total':'American Indian or Alaska Native Total',
                                         'EF1980_A.American Indian or Alaska native men':'American Indian or Alaska Native Men',
                                         'EF1980_A.American Indian or Alaska native women':'American Indian or Alaska Native Women',
                                         'ef1991_a.Race/ethnicity unknown total':'Race/Ethnicity Unknown Total',
                                         'EF1992_A.Race/ethnicity unknown men':'Race/Ethnicity Unknown Men',
                                         'EF1992_A.Race/ethnicity unknown women':'Race/Ethnicity Unknown Women',
                                         'EF1980_A.Nonresident alien total':'Nonresident Alien Total',
                                         'EF1980_A.Nonresident alien men':'Nonresident Alien Total Men',
                                         'EF1980_A.Nonresident alien women':'Nonresident Alien Total Women'})

# Drop Irrelevant Data from the Data Frame 
df_demographics= df_demographics.drop('unitid',axis=1)
df_demographics = df_demographics.drop(range(1,40,2))
df_demographics = df_demographics.drop('Unnamed: 28', axis=1)
df_demographics = df_demographics.drop('EF1980_A.Level of student', axis=1)
df_demographics = df_demographics.drop('institution name', axis=1)
df_demographics = df_demographics.reset_index(drop=True) # Reset Index

df_demographics
Out[2]:
Year Grand Total Grand Total Men Grand Total Women White Non-Hispanic Total White Non-Hispanic Men White Non-Hispanic Women Black Non-Hispanic Total Black Non-Hispanic Men Black Non-Hispanic Women Hispanic Total Hispanic Men Hispanic Women Asian or Pacific Islander Total Asian or Pacific Islander Men Asian or Pacific Islander Women American Indian or Alaska Native Total American Indian or Alaska Native Men American Indian or Alaska Native Women Race/Ethnicity Unknown Total Race/Ethnicity Unknown Men Race/Ethnicity Unknown Women Nonresident Alien Total Nonresident Alien Total Men Nonresident Alien Total Women
0 1980 6668 3768 2900 5224 2986 2238 405 208 197 434 257 177 418 210 208 38 20 18 NaN NaN NaN 149 87 62
1 1984 6634 3800 2834 4702 2739 1963 507 256 251 575 322 253 491 259 232 39 22 17 NaN NaN NaN 320 202 118
2 1986 6614 3751 2863 4508 2651 1857 511 241 270 624 345 279 645 314 331 46 25 21 NaN NaN NaN 280 175 105
3 1988 6506 3668 2838 4200 2458 1742 547 275 272 614 333 281 885 440 445 56 27 29 NaN NaN NaN NaN NaN NaN
4 1991 6640 3621 3019 3828 2206 1622 525 233 292 655 346 309 1324 647 677 79 33 46 53 NaN NaN 229 156 73
5 1992 6816 3631 3185 3773 2131 1642 512 208 304 701 362 339 1485 718 767 82 30 52 37 22 15 263 182 81
6 1993 7007 3662 3345 3760 2054 1706 514 219 295 741 387 354 1617 784 833 92 37 55 50 29 21 283 181 102
7 1994 7075 3681 3394 3567 1929 1638 520 212 308 727 366 361 1646 792 854 91 37 54 210 149 61 314 196 118
8 1995 6946 3430 3516 3562 1826 1736 531 218 313 775 368 407 1581 751 830 99 40 59 84 38 46 314 189 125
9 1996 6814 3345 3469 3546 1771 1775 506 216 290 732 363 369 1545 736 809 96 37 59 85 43 42 304 179 125
10 1997 7127 3510 3617 3755 1856 1899 524 230 294 719 356 363 1588 752 836 86 36 50 144 91 53 311 189 122
11 1998 7146 3489 3657 3821 1890 1931 528 233 295 722 369 353 1543 723 820 80 30 50 147 62 85 305 182 123
12 1999 7784 3797 3987 3729 1908 1821 554 251 303 710 359 351 1593 726 867 85 33 52 802 324 478 311 196 115
13 2000 7886 3779 4107 3750 1889 1861 578 261 317 682 328 354 1656 751 905 103 45 58 798 316 482 319 189 130
14 2001 7279 3597 3682 3701 1873 1828 572 265 307 718 348 370 1690 803 887 112 45 67 152 73 79 334 190 144
15 2002 7360 3610 3750 3567 1806 1761 649 304 345 772 361 411 1686 777 909 122 53 69 220 105 115 344 204 140
16 2003 7054 3520 3534 3105 1580 1525 682 315 367 807 374 433 1666 811 855 125 56 69 296 149 147 373 235 138
17 2004 6555 3430 3125 2693 1459 1234 698 335 363 768 369 399 1581 791 790 137 58 79 293 156 137 385 262 123
18 2005 6576 3463 3113 2678 1463 1215 688 339 349 739 360 379 1584 788 796 141 64 77 345 186 159 401 263 138
19 2006 6422 3338 3084 2590 1366 1224 661 333 328 727 355 372 1558 774 784 150 62 88 343 195 148 393 253 140
20 2007 6584 3392 3192 2721 1442 1279 623 310 313 757 385 372 1583 766 817 149 68 81 336 173 163 415 248 167
In [3]:
# Import the .csv File to Be Read 
df_demographics2 = pd.read_csv('Stanford Demographics 2010-2021.csv')
pd.set_option('display.max_columns',None)

# Drop Irrelevant Data from the Data Frame 

df_demographics2= df_demographics2.drop('unitid',axis=1)
df_demographics2 = df_demographics2.drop([0,2,4,6,8,10,12,14,16,18,20,22])
df_demographics2 = df_demographics2.drop('IDX_EF', axis=1)
df_demographics2 = df_demographics2.drop('Level of Student', axis=1)
df_demographics2 = df_demographics2.drop('Institution Name', axis=1)
df_demographics2 = df_demographics2.reset_index(drop=True) # Reset Index

#Check Data for Inconsisitencies 
df_demographics2.duplicated().sum()
df_demographics2.isnull().sum()

# Further Information about the DataFrame
#df_demographics.info()
df_demographics2
Out[3]:
Year Grand Total Grand Total Men Grand Total Women American Indian or Alaska Native Total American Indian or Alaska Native Men American Indian or Alaska Native Women Asian Total Asian Men Asian Women Black or African American Total Black or African American Men Black or African American Women Hispanic Total Hispanic Men Hispanic Women Native Hawaiian or Other Pacific Islander Total Native Hawaiian or Other Pacific Islander Men Native Hawaiian or Other Pacific Islander Women White Total White Men White Women Two or More Races Total Two or More Races Men Two or More Races Women Race/Ethnicity Unknown Total Race/Ethnicity Unknown Men Race/Ethnicity Unknown Women Nonresident Alien Total Nonresident Alien Men Nonresident Alien Women
0 2010 6940 3572 3368 78 36 42 1236 588 648 485 237 248 1081 566 515 29 14 15 2702 1451 1251 723 343 380 93 52 41 513 285 228
1 2011 6988 3608 3380 65 36 29 1260 622 638 509 249 260 1151 594 557 32 16 16 2562 1357 1205 802 375 427 80 47 33 527 312 215
2 2012 7063 3674 3389 69 40 29 1331 682 649 441 217 224 1179 632 547 26 13 13 2667 1398 1269 754 350 404 55 36 19 541 306 235
3 2013 7274 3743 3531 63 33 30 1340 700 640 444 231 213 1178 647 531 26 12 14 2660 1379 1281 753 354 399 245 37 208 565 350 215
4 2014 7019 3704 3315 80 41 39 1389 704 685 415 217 198 1128 641 487 27 13 14 2633 1340 1293 749 363 386 23 15 8 575 370 205
5 2015 7000 3667 3333 71 35 36 1432 724 708 425 212 213 1072 608 464 22 13 9 2614 1321 1293 727 348 379 24 17 7 613 389 224
6 2016 7034 3621 3413 75 40 35 1454 684 770 449 219 230 1117 630 487 24 13 11 2545 1304 1241 700 328 372 28 16 12 642 387 255
7 2017 7064 3513 3551 63 29 34 1529 684 845 471 218 253 1102 597 505 22 13 9 2518 1277 1241 679 310 369 31 19 12 649 366 283
8 2018 7087 3537 3550 56 26 30 1552 717 835 462 213 249 1113 583 530 22 14 8 2415 1244 1171 673 304 369 55 31 24 739 405 334
9 2019 6996 3468 3528 57 26 31 1590 736 854 477 228 249 1156 594 562 25 14 11 2264 1162 1102 653 293 360 26 17 9 748 398 350
10 2020 6366 3124 3242 62 27 35 1579 752 827 473 214 259 1082 547 535 22 14 8 1846 943 903 609 275 334 14 11 3 679 341 338
11 2021 7645 3778 3867 67 29 38 1917 951 966 547 260 287 1339 677 662 21 10 11 2139 1084 1055 778 338 440 19 15 4 818 414 404

Exploratory Data Analysis¶

In [4]:
# Analysis of Gender Breakdown from 1980 - 2007

fig_gender_bar = px.histogram(df_demographics,x="Year",y = ['Grand Total','Grand Total Men','Grand Total Women'],text_auto=True,barmode='group')
fig_gender_bar.update_layout(title_text = 'Analysis of Stanford Undergraduate Admissions from 1980-2007: Gender Breakdown', #Title of the Plot
                  legend_title = 'Legend',
                  xaxis_title = 'Year', #x-axis label
                  yaxis_title = 'Number of Students', #y-axis label
                  barmode='group', 
                  bargap=0.01, #Gap between bars of adjacent location
                  bargroupgap=0.01) #Gap between bars of the same location coordinates

fig_gender_bar.show()
In [5]:
# Analysis of Gender Breakdown from 1980 - 2007
fig_gender_line = px.line(df_demographics, x='Year', y=['Grand Total','Grand Total Men','Grand Total Women'], markers=True)
fig_gender_line.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
                 title_text = 'Analysis of Stanford Undergraduate Admissions from 1980-2007: Gender Breakdown', #Title of the Plot
                 legend_title = 'Legend',
                 xaxis_title = 'Year', #x-axis label
                 yaxis_title = 'Number of Students') #y-axis label # Updates the values from the dataframe from type object to numeric values

fig_gender_line.show()

When analyzing the enrollment of men to women over the years there is a steady increase in the enrollment of women from 1980 to 2000. In 1995 the number of women enrolled at Stanford surpassed the number of men enrolled in the institution for the first time in over a decade and continued to increase until 2000 where the number of women enrolled in Stanford peaked and started to decrease from there.

There are alot of factors that aren't taken into account within this study, but this data suggests that affirmitive action could have been a factor that positively impacted the number of women enrolled in this institution.

In [6]:
# Analysis of Gender Breakdown from 2010-2021

fig_gender_bar2 = px.histogram(df_demographics2,x='Year',y = ['Grand Total','Grand Total Men','Grand Total Women'],text_auto=True,barmode='group')
fig_gender_bar2.update_layout(title_text = 'Analysis of Stanford Undergraduate Admissions from 2010-2021: Gender Breakdown', #Title of the Plot
                  xaxis_title = 'Year', #x-axis label
                  yaxis_title = 'Number of Students', #y-axis label
                  legend_title = 'Legend',
                  barmode='group', 
                  bargap=0.01, #Gap between bars of adjacent location
                  bargroupgap=0.01) #Gap between bars of the same location coordinates


fig_gender_bar2.show()
In [7]:
# Analysis of Gender Breakdown from 2010-2021
fig_gender_line2 = px.line(df_demographics2, x='Year', y=['Grand Total','Grand Total Men','Grand Total Women'], markers=True)
fig_gender_line2.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
                 title_text = 'Analysis of Stanford Undergraduate Admissions from 2010-2021: Gender Breakdown', #Title of the Plot
                 legend_title = 'Legend',
                 xaxis_title = 'Year', #x-axis label
                 yaxis_title = 'Number of Students') #y-axis label # Updates the values from the dataframe from type object to numeric values

fig_gender_line2.show()

From 2010 to 2021 the amount of men and women enrolled at Stanford remained relatively consistent with only an anomoly showing in 2020 possibly due to the pandemic and the effects of Covid 19 on our society.

In [8]:
# Analysis of Race/Ethinicty Breakdown from 1980-2007
fig_race = px.histogram(df_demographics,x="Year",y = ['Grand Total','White Non-Hispanic Total','Black Non-Hispanic Total','Hispanic Total','Asian or Pacific Islander Total','American Indian or Alaska Native Total','Race/Ethnicity Unknown Total','Nonresident Alien Total'],text_auto=True,barmode='group')
fig_race.update_layout(title_text = 'Analysis of Stanford Undergraduate Admissions from 1980-2007- Race/Ethnicity Breakdown', #Title of the Plot
                  legend_title = 'Legend',
                  xaxis_title = 'Year', #x-axis label
                  yaxis_title = 'Number of Applicants', #y-axis label
                  barmode='group', 
                  bargap=0.01, #Gap between bars of adjacent location
                  bargroupgap=0.01) #Gap between bars of the same location coordinates
fig_race.show()
In [9]:
# Analysis of Race/Ethinicty Breakdown from 1980-2007
fig_race_line = px.line(df_demographics, x='Year', y = ['Grand Total','White Non-Hispanic Total','Black Non-Hispanic Total','Hispanic Total','Asian or Pacific Islander Total','American Indian or Alaska Native Total','Race/Ethnicity Unknown Total','Nonresident Alien Total'], markers=True)
fig_race_line.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
                 title_text = 'Analysis of Stanford Undergraduate Admissions from 1980-2007: Race/Ethnicity Breakdown', #Title of the Plot
                 legend_title = 'Legend',
                 xaxis_title = 'Year', #x-axis label
                 yaxis_title = 'Number of Applicants') #y-axis label # Updates the values from the dataframe from type object to numeric values

fig_race_line.show()

When analyzing the break down of race and ethnicity of the undergraduate population at Stanford from 1980 to 2007 the results show that the two races with significant changes were the White Non-Hispanic total and the Asian or Pacific Islander total. All other races/ethnicities stayed roughly the same over the years.

There are alot of factors that aren't taken into account within this study such as the possible influence of an increase in the Asian demographic in the United States or the number of applicants from each demographic recieved, but this data suggests that affirmitive action could have been a factor that positively impacted the number of Asian and Pacific Islander total at this institution.

In [10]:
# Analysis of Race/Ethinicty Breakdown from 2010-2021
fig_race2 = px.histogram(df_demographics2,x="Year",y = ['Grand Total','American Indian or Alaska Native Total','Asian Total ','Black or African American Total','Hispanic Total','Native Hawaiian or Other Pacific Islander Total','White Total','Two or More Races Total','Race/Ethnicity Unknown Total','Nonresident Alien Total'],text_auto=True,barmode='group')
fig_race2.update_layout(title_text = 'Analysis of Stanford Undergraduate Admissions from 2010-2021: Race/Ethnicity Breakdown', #Title of the Plot
                  xaxis_title = 'Year', #x-axis label
                  yaxis_title = 'Number of Applicants', #y-axis label
                  legend_title = 'Legend',      
                  barmode='group', 
                  bargap=0.01, #Gap between bars of adjacent location
                  bargroupgap=0.01) #Gap between bars of the same location coordinates
fig_race2.show()
In [11]:
# Analysis of Race/Ethinicty Breakdown from 2010-2021
fig_race_line = px.line(df_demographics2, x='Year', y = ['Grand Total','American Indian or Alaska Native Total','Asian Total ','Black or African American Total','Hispanic Total','Native Hawaiian or Other Pacific Islander Total','White Total','Two or More Races Total','Race/Ethnicity Unknown Total','Nonresident Alien Total'], markers=True)
fig_race_line.update_layout(autotypenumbers='convert types', # Updates the values from the dataframe from type object to numeric values
                 title_text = 'Analysis of Stanford Undergraduate Admissions from 2010-2021: Race/Ethnicity Breakdown', #Title of the Plot
                 xaxis_title = 'Year', #x-axis label
                 legend_title = 'Legend',           
                 yaxis_title = 'Number of Applicants') #y-axis label # Updates the values from the dataframe from type object to numeric values

fig_race_line.show()

When analyzing the break down of race and ethnicity of the undergraduate population at Stanford from 2010 to 2021 the results show that there aren't significant changes in the number of students in each race/ethnicity.

In [12]:
# Analysis of Race/Ethinicty Percentages at Stanford 1980 Compared to U.S Populations 

# Pie Chart of Race Percentages at Stanford 1980
fig_race_1980_Stanford = px.pie(df_demographics, values=[df_demographics['White Non-Hispanic Total'].values[0],df_demographics['Black Non-Hispanic Total'].values[0],df_demographics['Hispanic Total'].values[0],df_demographics['Asian or Pacific Islander Total'].values[0],df_demographics['American Indian or Alaska Native Total'].values[0],df_demographics['Race/Ethnicity Unknown Total'].values[0],df_demographics['Nonresident Alien Total'].values[0]],
             names=['White','Black','Hispanic','Asian or Pacific Islander','American Indian or Alaska Native','Race/Ethnicity Unknown','Nonresident Alien Total'],)
fig_race_1980_Stanford.update_traces(textposition='inside', textinfo='percent+label',title_text = '1980 Stanford Racial/Ethnic Demographics of Undergraduate Admissions')
fig_race_1980_Stanford.show()

# Pie Chart of Race Percentages of U.S. Population 

# Initialie Data of Lists
US_population1980 = { 'Race': ['White', 'Black','American Indian or Alaska Native', 'Asian and Pacific Islander','Other Races','Hispanic'],
                    'Percent': ['83.1','11.7','0.6','1.5','3.0','6.4']} # Data for the U.S Population came from the U.S Department of Commerce Buereau of the Census 
df_US_population1980 = pd.DataFrame(US_population1980)

# Pie Chart of Race Percentages of U.S Population
fig_race_population1980 = px.pie(df_US_population1980, values= 'Percent',
             names='Race')
fig_race_population1980.update_traces(textposition='inside', textinfo='percent+label',title_text = '1980 Racial/Ethnic Demographics of the United States Population')
fig_race_population1980.show()

When analyzing the racial/ethnic percentage breakdown of the Stanford demographic compared to the racial/ethnic demographic of the United States population in 1980, the data shows:

  • Whites made up 78.2% of the U.S population and made up an equal part of the Stanford population with 78.3%
  • Hispanics made up 6.02% of the population and made up relatively similar percentage with 6.51% of the Stanford population
  • Blacks made up 11% of the U.S population while only comprising 6.07% of the Stanford population
  • Asians/Pacific Islanders made up 1.41% of the U.S population while makeing up 6.27% of the Stanford population
In [13]:
# Analysis of Race/Ethinicty Percentages at Stanford 2010 Compared to U.S Populations 

# Pie Chart of Race Percentages at Stanford 2010
fig_race_2010_Stanford = px.pie(df_demographics2, values=[df_demographics2['White Total'].values[0],df_demographics2['Black or African American Total'].values[0],df_demographics2['Hispanic Total'].values[0],df_demographics2['Asian Total '].values[0],df_demographics2['American Indian or Alaska Native Total'].values[0],df_demographics2['Native Hawaiian or Other Pacific Islander Total'].values[0],df_demographics2['Two or More Races Total'].values[0],df_demographics2['Race/Ethnicity Unknown Total'].values[0],df_demographics2['Nonresident Alien Total'].values[0]],
             names=['White','Black or African American','Hispanic','Asian','American Indian or Alaska Native','Native Hawaiian or Other Pacific Islander Total','Two or More Races Total','Race/Ethnicity Unknown','Nonresident Alien'],)
fig_race_2010_Stanford.update_traces(textposition='inside', textinfo='percent+label',title_text = '2010 Stanford Racial Demographics of Undergraduate Admissions')
fig_race_2010_Stanford.show()

# Pie Chart of Race Percentages of U.S. Population 

# Initialie Data of Lists
US_population2010 = { 'Race': ['White', 'Black or African American','American Indian or Alaska Native', 'Asian','Native Hawaiian and Other Pacific Islander','Hispanic/Latino','Multiracial'],
                    'Percent': ['63.8','12.3','0.7','4.8','0.2','16.4','1.8']}
df_US_population2010= pd.DataFrame(US_population2010)

# Pie Chart of Race Percentages of U.S Population
fig_race_population2010 = px.pie(df_US_population2010, values= 'Percent',
             names='Race')
fig_race_population2010.update_traces(textposition='inside', textinfo='percent+label',title_text = '2010 Racial Demographics of the United States Population')
fig_race_population2010.show()

When analyzing the racial/ethnic percentage breakdown of the Stanford demographic compared to the racial/ethnic demographic of the United States population in 2010, the data shows:

  • Whites made up 63.8% of the U.S population while comprising of 38.9% of the Stanford population
  • Hispanics made up 16.4% of the population while comprising 15.6% of the Stanford population
  • Blacks made up 12.3% of the U.S population while only comprising 6.99% of the Stanford population
  • Asians made up 4.8% of the U.S population while making up 17.8% of the Stanford population

Conclusion¶

In summary, this comprehensive study explored the profound impact of affirmative action, or its absence, on Stanford University's demographics spanning the years from 1980 to 2021. As of June 2023, with the Supreme Court ruling against race-conscious admission practices in higher education, the findings of this analysis become even more pertinent for understanding the evolving landscape of diversity and equal opportunities in academia.

The study's key findings revealed significant shifts in gender and racial demographics over the decades. Initially, we observed a steady increase in female enrollment at Stanford, with women surpassing men in 1995, possibly indicative of affirmative action's influence in promoting gender diversity. However, the subsequent decline in female enrollment after 2000 underscores the need for nuanced analysis that considers various contributing factors beyond policy changes.

Furthermore, when comparing Stanford's demographic percentages to the U.S. population in both 1980 and 2010, stark disparities emerge, notably in the overrepresentation of Asians and the underrepresentation of Blacks at the university. Although it is vital to acknowledge the influence of external factors, such as demographic shifts and application rates, on these changes, these findings underscore the persistent challenges of achieving equitable representation and equal opportunities in higher education.

In light of these results, this study contributes to a broader understanding of the complexities surrounding affirmative action and its role in shaping university demographics. It prompts critical reflection on the need for comprehensive policies that address systemic disparities and promote inclusive education. As the landscape of higher education continues to evolve, this analysis serves as a valuable reference point for ongoing discussions and actions aimed at fostering diversity, equality, and access within our academic institutions.

Citations¶

https://usafacts.org/data/topics/people-society/population-and-demographics/our-changing-population/